A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets

Y. Zhang, Nirvana Meratnia, Paul J.M. Havinga

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    The term "outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous applications domains. In this paper, we report on contemporary unsupervised outlier detection techniques for multiple types of data sets and provide a comprehensive taxonomy framework and two decision trees to select the most suitable technique based on data set. Furthermore, we highlight the advantages, disadvantages and performance issues of each class of outlier detection techniques under this taxonomy framework.
    Original languageUndefined
    Place of PublicationEnschede
    PublisherCentre for Telematics and Information Technology (CTIT)
    Number of pages40
    Publication statusPublished - 14 Nov 2007

    Publication series

    NameCTIT Technical Report Series
    PublisherCentre for Telematics and Information Technology, University of Twente
    No.Paper P-NS/TR-CTIT-07-79
    ISSN (Print)1381-3625


    • METIS-245767
    • IR-64450
    • CAES-PS: Pervasive Systems
    • EWI-11366

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